Computation Rate Maximization for Intelligent Reflecting Surface Enhanced Wireless Powered Mobile Edge Computing Networks
Sun Mao, Ning Zhang, Lei Liu, Jinsong Wu, Mianxiong Dong, Kaoru Ota, Tang Liu, Dié Wu
Abstract
The combination of wireless energy transfer (WET) and mobile edge computing (MEC) has been proposed to satisfy the energy supply and computation requirements of resource-constrained Internet of Things (IoT) devices. However, the energy transfer efficiency and task offloading rate cannot be guaranteed when wireless links between the hybrid access point (HAP) and IoT devices are hostile. To address this problem, this paper aims at utilizing the intelligent reflecting surfaces (IRS) technique to improve the efficiency of WET and task offloading. In particular, we investigate the total computation bits maximization problem for IRS-enhanced wireless powered MEC networks, by jointly optimizing the downlink/uplink phase beamforming of IRS, transmission power and time slot assignment used for WET and task offloading, and local computing frequencies of IoT devices. Furthermore, an iterative algorithm is presented to solve the non-convex non-linear optimization problem, while the optimal transmission power and time allocation, uplink phase beamforming matrixes and local computing frequencies are derived in closed-form expressions. Finally, extensive simulation results validate that our proposed IRS-enhanced wireless powered MEC strategy can achieve higher total computation rate as compared to existing baseline schemes.